The hidden potential in every piece of content
When large archives are not intelligently organized, finding and reusing the right content becomes slow and complex. At the same time, limited insight into what truly engages audiences makes creative and strategic decisions harder. Combined with the need to adapt material for different formats, audiences and platforms, this fragmentation consumes teams' time and prevents the full value of an archive from becoming reach, relevance and new revenue opportunities.
Key challenges and how we solve them
- Intelligent search across large archivesFind content by meaning and context, even when users don't know the title or exact words.
- Repurposing content across formatsUse AI to turn existing content into new formats, clips, summaries and assets for different channels and touchpoints.
- Performance forecastingCombine historical, behavioral and audience data to identify patterns and inform editorial, format and distribution decisions.
- Revenue per user or subscriberPersonalize experiences and catalog discovery with more relevant offers.
- Recommendation modelsRecommend content based on each user's viewing history, interactions and events.
- Audience engagement and retentionCombine behavior, consumption and context to identify patterns and personalize content, recommendations and experiences.
How can we help?
Use AI and data to produce, distribute and optimize content at scale, understand your audience, anticipate trends and turn each piece of content into business results.
- Intelligent archive searchFind scenes, topics, people and content by context and meaning without relying on perfect titles, tags or metadata.
- Intelligent content adaptationTurn existing videos, programs and materials into clips, newsletters, podcasts and new formats for different channels.
- Audience and content analyticsCentralize media, audience and conversion data to track reach, share of voice, brand growth, lead generation and the business impact of each piece of content.
- Audience personalization and monetizationUse behavioral data to personalize experiences, offers and journeys that increase conversion, revenue per user and repeat engagement.
- Recommendation enginesRecommend content, products and experiences based on each user's history, context and interests.
- AI-powered engagement and retentionIdentify signs of declining interest and personalize content, frequency and experience to increase repeat engagement and reduce churn.
Return on
investment, in practice.
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